Bauer, C., Carterette, B., Ferro, N., Fuhr, N., Beel, J., Breuer, T., … Zobel, J. (2023). Report on the Dagstuhl Seminar on Frontiers of Information Access Experimentation for Research and Education SIGIR Forum, 57, 1-7. https://doi.org/10.1145/3636341.3636351
References
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2023
Clarke, C., Diaz, F., & Arabzadeh, N. (2023). Preference-Based Offline Evaluation Presented at the Preference-Based Offline Evaluation conference. https://doi.org/10.1145/3539597.3572725
Huo, S., Arabzadeh, N., & Clarke, C. (2023). Retrieving Supporting Evidence for Generative Question Answering ArXiv, abs/2309.11392. https://doi.org/10.48550/arXiv.2309.11392
Hebert, L., Golab, L., & Cohen, R. (2023). Predicting Hateful Discussions on Reddit Using Graph Transformer Networks And Communal Context ArXiv, abs/2301.04248. https://doi.org/10.48550/arXiv.2301.04248
Lin, S.-C., Li, M., & Lin, J. (2023). Aggretriever: A Simple Approach to Aggregate Textual Representations For Robust Dense Passage Retrieval Transactions of the Association for Computational Linguistics, 11, 436-452. https://doi.org/10.1162/TACL_A_00556
Ozsu, T. (2023). Data Science: A Systematic Treatment ArXiv, abs/2301.13761. https://doi.org/10.48550/arXiv.2301.13761
Chen, H., Lassance, C., & Lin, J. (2023). End-to-End Retrieval With Learned Dense and Sparse Representations Using Lucene ArXiv, abs/2311.18503. https://doi.org/10.48550/ARXIV.2311.18503
Lin, S.-C., Ahmad, A., & Lin, J. (2023). mAggretriever: A Simple Yet Effective Approach to Zero-Shot Multilingual Dense Retrieval Presented at the MAggretriever: A Simple Yet Effective Approach to Zero-Shot Multilingual Dense Retrieval conference. Retrieved from https://aclanthology.org/2023.emnlp-main.715
Pradeep, R., Sharifymoghaddam, S., & Lin, J. (2023). RankVicuna: Zero-Shot Listwise Document Reranking With Open-Source Large Language Models ArXiv, abs/2309.15088. https://doi.org/10.48550/ARXIV.2309.15088
Oladipo, A., Adeyemi, M., Ahia, O., Owodunni, A. T., Ogundepo, O., Adelani, D. I., & Lin, J. (2023). Better Quality Pre-Training Data and T5 Models for African Languages Presented at the Better Quality Pre-Training Data and T5 Models for African Languages conference. Retrieved from https://aclanthology.org/2023.emnlp-main.11